Discover the power of semantic search! Learn how AI, NLP, and ML enhance search accuracy by understanding user intent and context.
Semantic search is an advanced information retrieval technique that aims to understand the intent and contextual meaning behind a user's query, rather than simply matching keywords. Unlike traditional search engines that rely on literal term matching, semantic search uses Natural Language Processing (NLP) and Machine Learning (ML) to deliver more relevant and accurate results. The goal is to bridge the gap between human language and the structured data that computers can easily process, leading to a more intuitive and effective search experience.
This technology is powered by converting unstructured data like text or images into numerical representations called embeddings. Models such as OpenAI's CLIP (Contrastive Language-Image Pre-Training) or Google's BERT are trained to generate these embeddings, which capture the semantic essence of the content. Once data is represented as vectors, a vector database like Milvus or Weaviate uses specialized algorithms like Meta's FAISS to perform a rapid and efficient similarity search.
Semantic search has transformed how we interact with digital information across various domains. Two prominent examples include: